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The Dark Energy Spectroscopic Instrument (DESI) completed its 5 month Survey Validation in 2021 May. Spectra of stellar and extragalactic targets from Survey Validation constitute the first major data sample from the DESI survey. This paper describes the public release of those spectra, the catalogs of derived properties, and the intermediate data products. In total, the public release includes good-quality spectral information from 466,447 objects targeted as part of the Milky Way Survey, 428,758 as part of the Bright Galaxy Survey, 227,318 as part of the Luminous Red Galaxy sample, 437,664 as part of the Emission Line Galaxy sample, and 76,079 as part of the Quasar sample. In addition, the release includes spectral information from 137,148 objects that expand the scope beyond the primary samples as part of a series of secondary programs. Here, we describe the spectral data, data quality, data products, Large-Scale Structure science catalogs, access to the data, and references that provide relevant background to using these spectra.

Context. The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches is machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples since they are key factors in the estimation of reliable photo-zs. Aims. The goal of this work is to calculate the photo-zs for the Year 3 (Y3) Dark Energy Survey (DES) Deep Fields catalogue using the Directional Neighborhood Fitting (DNF) machine learning algorithm. Moreover, we want to develop techniques to assess the incompleteness of the training sample and metrics to study how incompleteness affects the quality of photometric redshifts. Finally, we are interested in comparing the performance obtained by DNF on the Y3 DES Deep Fields catalogue with that of the EAzY template fitting approach. Methods. We emulated – at a brighter magnitude – the training incompleteness with a spectroscopic sample whose redshifts are known to have a measurable view of the problem. We used a principal component analysis to graphically assess the incompleteness and relate it with the performance parameters provided by DNF. Finally, we applied the results on the incompleteness to the photo-z computation on the Y3 DES Deep Fields with DNF and estimated its performance. Results. The photo-zs of the galaxies in the DES deep fields were computed with the DNF algorithm and added to the Y3 DES Deep Fields catalogue. We have developed some techniques to evaluate the performance in the absence of “true” redshift and to assess the completeness. We have studied the tradeoff in the training sample between the highest spectroscopic redshift quality versus completeness. We found some advantages in relaxing the highest-quality spectroscopic redshift requirements at fainter magnitudes in favour of completeness. The results achieved by DNF on the Y3 Deep Fields are competitive with the ones provided by EAzY, showing notable stability at high redshifts. It should be noted that the good results obtained by DNF in the estimation of photo-zs in deep field catalogues make DNF suitable for the future Legacy Survey of Space and Time (LSST) and Euclid data, which will have similar depths to the Y3 DES Deep Fields.

The Dark Energy Spectroscopic Instrument (DESI) was designed to conduct a survey covering 14,000 deg2 over 5 yr to constrain the cosmic expansion history through precise measurements of baryon acoustic oscillations (BAO). The scientific program for DESI was evaluated during a 5 month survey validation (SV) campaign before beginning full operations. This program produced deep spectra of tens of thousands of objects from each of the stellar Milky Way Survey (MWS), Bright Galaxy Survey (BGS), luminous red galaxy (LRG), emission line galaxy (ELG), and quasar target classes. These SV spectra were used to optimize redshift distributions, characterize exposure times, determine calibration procedures, and assess observational overheads for the 5 yr program. In this paper, we present the final target selection algorithms, redshift distributions, and projected cosmology constraints resulting from those studies. We also present a One-Percent Survey conducted at the conclusion of SV covering 140 deg2 using the final target selection algorithms with exposures of a depth typical of the main survey. The SV indicates that DESI will be able to complete the full 14,000 deg2 program with spectroscopically confirmed targets from the MWS, BGS, LRG, ELG, and quasar programs with total sample sizes of 7.2, 13.8, 7.46, 15.7, and 2.87 million, respectively. These samples will allow exploration of the Milky Way halo, clustering on all scales, and BAO measurements with a statistical precision of 0.28% over the redshift interval z < 1.1, 0.39% over the redshift interval 1.1 < z < 1.9, and 0.46% over the redshift interval 1.9 < z < 3.5.

Objectives: Telecritical care (TCC) refers to the delivery of critical care using telehealth technologies. Despite increasing utilization, significant practice variation exists and literature regarding efficacy remains sparse. The Telecritical Care Collaborative Network sought to provide expert, consensus-based best practice recommendations for the design and delivery of TCC., Design: We used a modified Delphi methodology. Following literature review, an oversight panel identified core domains and developed declarative statements for review by an expert voting panel. During three voting rounds, voters agreed or disagreed with statements and provided open-ended feedback, which the oversight panel used to revise statements. Statements met criteria for consensus when accepted by greater than or equal to 85% of voters., Setting/subjects: The oversight panel included 18 multidisciplinary members of the TCC Collaborative Network, and the voting panel included 32 invited experts in TCC, emphasizing diversity of discipline, care delivery models, and geography., Interventions: None., Measurements and Main Results: We identified ten core domains: definitions/terminology; care delivery models; staffing and coverage models; technological considerations; ergonomics and workplace safety; licensing, credentialing, and certification; trust and relationship building; quality, safety, and efficiency, research agenda; and advocacy, leading to 79 practice statements. Of 79 original statements, 67 were accepted in round 1. After revision, nine were accepted in round 2 and two in round 3 (two statements were merged). In total, 78 practice statements achieved expert consensus., Conclusions: These expert consensus recommendations cover a broad range of topics relevant to delivery of TCC. Experts agreed that TCC is most effective when delivered by care teams with specific expertise and by programs with explicit protocols focusing on effective communication, technical reliability, and real-time availability. Interventions should be tailored to local conditions. Although further research is needed to guide future best practice statements, these results provide valuable and actionable recommendations for the delivery of high-quality TCC., Competing Interests: Dr. Singh received funding from Intuitive Surgical. Dr. Laudanski’s institution received funding from the National Institutes of Health. Dr. Rincon received funding from Blue Cirrus Consulting, Baxter Healthcare, Phillips Health, and Viven Health. Dr. Villanueva received funding from Intercept Telemed, Banner Health, Pasadena Law Center; he disclosed that he is the Chief Medical Officer of Intercept Telemed, a telecritical care medicine physician for Banner Health; and he owns stock in Omnicure and Fruit Street Health, Public Benefit Corporation, and owns stock in Google, Amazon, Apple, Edwards Lifesciences, Eli Lilly, Johnson and Johnson, Microsoft, Oracle, Palantir, UnitedHealth Group, Berkshire Hathaway, Proctor and Gamble, and Walmart. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

This report describes an innovative interprofessional education collaborative practice (IPCP) experience for rehabilitation professions students using a unique on-campus camp model through a community-academic partnership. Throughout the three-day camp, known as the Bright Ideas TBI Camp, interprofessional student groups deliver tailored health and wellness services to individuals with disabilities due to traumatic brain injury and their caregivers. Initial program evaluation suggests that this camp model offers an effective IPCP experience for students while addressing community health needs. Further outcome evaluation is needed to determine the impact of the camp on students' development of IPCP competencies and health outcomes of clients and caregivers.

Vesicular stomatitis (VS) is a viral disease that affects horses, cattle, and swine that is transmitted by direct contact and hematophagous insects. In 2023, a multi-state outbreak of vesicular stomatitis New Jersey virus (VSNJV) occurred in California, Nevada, and Texas, infecting horses, cattle, and rhinoceros. To identify possible insect vectors, we conducted insect surveillance at various locations in San Diego County, CA, including at a wildlife park. CO 2 baited traps set from mid-May to mid-August 2023 collected 2357 Culicoides biting midges and 1215 Simulium black flies, which are insect genera implicated in VSNJV transmission. Insects were pooled by species, location, and date, then tested for viral RNA. Nine RNA-positive pools of Culicoides spp. and sixteen RNA-positive pools of Simulium spp were detected. Infectious virus was detected by cytopathic effect in 96% of the RNA-positive pools. This is the first report of VSNJV in wild-caught C. bergi , C. freeborni , C. occidentalis , S. argus , S. hippovorum , and S. tescorum. The vector competency of these species for VSNJV has yet to be determined but warrants examination. Active vector surveillance and testing during disease outbreaks increases our understanding of the ecology and epidemiology of VS and informs vector control efforts.

Purpose: To investigate bolus design and VMAT optimization settings for total scalp irradiation., Methods: Three silicone bolus designs (flat, hat, and custom) from .decimal were evaluated for adherence to five anthropomorphic head phantoms. Flat bolus was cut from a silicone sheet. Generic hat bolus resembles an elongated swim cap while custom bolus is manufactured by injecting silicone into a 3D printed mold. Bolus placement time was recorded. Air gaps between bolus and scalp were quantified on CT images. The dosimetric effect of air gaps on target coverage was evaluated in a treatment planning study where the scalp was planned to 60 Gy in 30 fractions. A noncoplanar VMAT technique based on gEUD penalties was investigated that explored the full range of gEUD alpha values to determine which settings achieve sufficient target coverage while minimizing brain dose. ANOVA and the t-test were used to evaluate statistically significant differences (threshold = 0.05)., Results: The flat bolus took 32 ± 5.9 min to construct and place, which was significantly longer (p < 0.001) compared with 0.67 ± 0.2 min for the generic hat bolus or 0.53 ± 0.10 min for the custom bolus. The air gap volumes were 38 ± 9.3 cc, 32 ± 14 cc, and 17 ± 7.0 cc for the flat, hat, and custom boluses, respectively. While the air gap differences between the flat and custom boluses were significant (p = 0.011), there were no significant dosimetric differences in PTV coverage at V57Gy or V60Gy. In the VMAT optimization study, a gEUD alpha of 2 was found to minimize the mean brain dose., Conclusions: Two challenging aspects of total scalp irradiation were investigated: bolus design and plan optimization. Results from this study show opportunities to shorten bolus fabrication time during simulation and create high quality treatment plans using a straightforward VMAT template with simple optimization settings., (© 2024 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Objectives: Study objectives were to explore nurses' perceptions of self-care, co-worker, and leader caring within healthcare work environments and assess reliability of 3 Watson Caritas Scores., Background: Assessing caring in an organization where Watson's Theory of Human Caring guides nursing practice offers insight into the professional practice environment., Method: This study reports quantitative data from mixed-methods descriptive, cross-sectional survey of 1307 RNs at a large healthcare system., Results: Mean scores were self = 5.46, co-worker = 5.39, and leader score = 5.53, and median scores were >5.6 (range, 1-7). All scales had a positive and significant correlation to likelihood to recommend the organization, with the largest being feeling cared for by leaders. Internal reliability of the 3 scales was ≥0.9., Conclusion: Nurse perceptions of caring may influence the organization's reputation. Assessing the perception of caring among nursing staff after the introduction and enculturation of this framework is needed. Results support psychometric value for 3 Watson Caritas Scores., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)